{"id":"W2295840922","doi":"","title":"IIIT Hyderabad in Summarization and Knowledge Base Population at TAC 2011","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Computer science; Task (project management); Natural language processing; Population; Knowledge base; Word (group theory); Artificial intelligence; Information retrieval; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00356612,0.0004791595,0.0005648478,0.001892246,0.001326368,0.002082173,0.001003847,0.0007195577,0.01004039],"category_scores_gemma":[0.006107065,0.0003009883,0.0003160266,0.002295393,0.000257319,0.001925997,0.00110437,0.0009415905,0.006855689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142053,"about_ca_system_score_gemma":0.001898274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01342253,"about_ca_topic_score_gemma":0.0153059,"domain_scores_codex":[0.9979685,0.0006258843,0.0001161824,0.0004524358,0.0005879719,0.0002490493],"domain_scores_gemma":[0.99528,0.001094439,0.0002351902,0.0008339203,0.001780472,0.0007759646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001385838,0.0008793477,0.01308746,0.000455615,0.000110738,0.001112724,0.004126031,0.01377497,0.03876005,0.003701479,0.17079,0.7518159],"study_design_scores_gemma":[0.0003176199,0.001964838,0.05104806,0.000112079,0.0002081388,0.0008230113,0.003857833,0.1007928,0.1379387,0.004853018,0.6977644,0.0003195452],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5872849,0.002981938,0.2039246,0.007725568,0.001517642,0.002117544,0.03815408,0.04764862,0.1086452],"genre_scores_gemma":[0.6160019,0.0007918514,0.1975893,0.0005618895,0.0003747641,0.000598605,0.06976639,0.002227747,0.1120876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01342253,"threshold_uncertainty_score":0.03358847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901822072906967,"score_gpt":0.2386160547581415,"score_spread":0.2195978340290718,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}